Machine Learning Engineer

Job Description:

We are seeking a highly skilled Machine Learning Engineer II to join our team remotely on a contract basis. In this role, you will be responsible for developing and optimizing algorithms for signal processing and machine learning tasks, particularly in the context of Noise Reduction for embedded systems. You will collaborate with cross-functional teams to design, test, and implement solutions that are effective in real-world environments, ensuring the integration of DSP and ML techniques.

Key Responsibilities:

• Develop and implement Digital Signal Processing (DSP) algorithms using MATLAB.
• Design, train, and deploy Machine Learning (ML) models in Python for noise reduction and signal enhancement tasks.
• Write C code for embedded systems and hardware/software interfaces related to signal processing.
• Optimize and improve existing DSP and ML pipelines for efficiency, scalability, and performance.
• Collaborate with software and hardware engineers to ensure integration of DSP and ML algorithms in embedded systems.
• Conduct Noise Reduction tasks to remove unwanted signals and enhance signal quality in real-time applications.
• Perform rigorous testing, validation, and optimization of models and algorithms.
• Document and communicate technical results effectively to both technical and non-technical stakeholders.

Required Skills & Expertise:

Core Skills:

• Digital Signal Processing (DSP): Deep understanding of DSP concepts, including filtering, spectral analysis, and signal transformations.
• Machine Learning (ML): Practical experience in developing and implementing machine learning models for real-world applications.

Technical Proficiencies:

• MATLAB: Extensive experience using MATLAB for DSP algorithm development and analysis.
• Python: Proficient in Python for developing and deploying machine learning models and data analysis.
• C Programming: Essential for developing software for embedded systems, with a focus on signal processing.
• Noise Reduction Techniques: Practical experience in noise reduction techniques, such as adaptive filtering, Wiener filtering, spectral subtraction, etc., applied in DSP and ML contexts.

Preferred Qualifications:

• Experience with real-time signal processing in embedded systems.
• Familiarity with GPU-based computing or frameworks like TensorFlow or PyTorch for ML model deployment.
• Understanding of time-series analysis and sensor data processing.